Image stitching is the process of combining data from multiple images to form a larger composite image or mosaic. This is possible when the amount of parallax between the images is small or zero, such as when the camera capturing the images is rotating about a point.
Recent successful approaches to image stitching have used feature based techniques to identify matching points between overlapping images, which are used to align the images. These methods typically employ random sampling algorithms such as RANSAC for robust estimation of the image geometry and to cope with noisy and outlier contaminated feature matches. The RANSAC step has an inner loop consisting of a fast solution for the parameters of interest, such as focal length, given a small number of correspondences. Since the probability of choosing a set of correct correspondences decreases rapidly as the sample size increases, solutions that use as few points as possible in the RANSAC loop are favorable. Currently the state of the art approaches use a 4 point linear solution for the homography in the RANSAC loop.